Model-based SIRR Methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]} formulate the image reflection removal problem as an optimization problem and solve it with optimization tools.
The gradient sparsity prior of natural images has been exploited in [1]}, [2]} to obtain decomposition with minimal edges and local features. The relative smoothness prior has been proposed in [3]} since the reflected image is usually more blurred.
In [6]}, a convex model which implies a partial differential equation with gradient thresholding is used to suppress the reflection from a single input image. The Laplacian fidelity prior and the \(l_0\)  gradient sparsity prior have been used in [5]} to formulate the optimization problem for reflection suppression. In [4]}, Gaussian Mixture Model (GMM) has been applied for modelling patch prior to exploit the ghosting effects on reflection.
